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Yufeng He
95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from typing import cast
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from agent_framework import Agent, AgentResponse, Message
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import SequentialBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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"""
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Sample: Sequential workflow (agent-focused API) with shared conversation context
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Build a high-level sequential workflow using SequentialBuilder and two domain agents.
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The shared conversation flows through each participant. Each agent appends its
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assistant message to the context. The sample prints the original user message plus
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the visible outputs from both agents.
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Note on internal adapters:
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- Sequential orchestration includes small adapter nodes for input normalization
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("input-conversation"), agent-response conversion ("to-conversation:<participant>"),
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and completion ("complete"). These may appear as ExecutorInvoke/Completed events in
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the stream—similar to how concurrent orchestration includes a dispatcher/aggregator.
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You can safely ignore them when focusing on agent progress.
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Prerequisites:
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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"""
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async def main() -> None:
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# 1) Create agents
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],
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credential=AzureCliCredential(),
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)
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writer = Agent(
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client=client,
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instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
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name="writer",
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)
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reviewer = Agent(
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client=client,
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instructions=("You are a thoughtful reviewer. Give brief feedback on the previous assistant message."),
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name="reviewer",
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)
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# 2) Build sequential workflow: writer -> reviewer
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workflow = SequentialBuilder(participants=[writer, reviewer], output_from="all").build()
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# 3) Run and collect outputs
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prompt = "Write a tagline for a budget-friendly eBike."
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result = await workflow.run(prompt)
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conversation = [Message(role="user", contents=[prompt])]
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for output in result.get_outputs():
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response = cast(AgentResponse, output)
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conversation.extend(response.messages)
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if conversation:
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print("===== Final Conversation =====")
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for i, msg in enumerate(conversation, start=1):
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name = msg.author_name or ("assistant" if msg.role == "assistant" else "user")
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print(f"{'-' * 60}\n{i:02d} [{name}]\n{msg.text}")
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"""
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Sample Output:
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===== Final Conversation =====
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------------------------------------------------------------
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01 [user]
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Write a tagline for a budget-friendly eBike.
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------------------------------------------------------------
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02 [writer]
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Ride farther, spend less—your affordable eBike adventure starts here.
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------------------------------------------------------------
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03 [reviewer]
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This tagline clearly communicates affordability and the benefit of extended travel, making it
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appealing to budget-conscious consumers. It has a friendly and motivating tone, though it could
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be slightly shorter for more punch. Overall, a strong and effective suggestion!
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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